Showing papers in "Journal of Neural Engineering in 2014"

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

Abstract: Objective. A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. Approach. The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. Main results. In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. Significance. The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

Robust artifactual independent component classification for BCI practitioners

Abstract: Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.

Mechanically-compliant intracortical implants reduce the neuroinflammatory response

Abstract: Objective. The mechanisms underlying intracortical microelectrode encapsulation and failure are not well understood. A leading hypothesis implicates the role of the mechanical mismatch between rigid implant materials and the much softer brain tissue. Previous work has established the benefits of compliant materials on reducing early neuroinflammatory events. However, recent studies established late onset of a disease-like neurodegenerative state. Approach. In this study, we implanted mechanically-adaptive materials, which are initially rigid but become compliant after implantation, to investigate the long-term chronic neuroinflammatory response to compliant intracortical microelectrodes. Main results. Three days after implantation, during the acute healing phase of the response, the tissue response to the compliant implants was statistically similar to that of chemically matched stiff implants with much higher rigidity. However, at two, eight, and sixteen weeks post-implantation in the rat cortex, the compliant implants demonstrated a significantly reduced neuroinflammatory response when compared to stiff reference materials. Chronically implanted compliant materials also exhibited a more stable blood-brain barrier than the stiff reference materials. Significance. Overall, the data show strikingly that mechanically-compliant intracortical implants can reduce the neuroinflammatory response in comparison to stiffer systems.

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Abstract: Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Usability of four commercially-oriented EEG systems.

Abstract: Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi’s ActiveTwo, which serves as an established laboratory-grade ‘gold standard’ baseline. The wireless systems compared include Advanced Brain Monitoring’s B-Alert X10, Emotiv Systems’ EPOC and the 2009 version of QUASAR’s Dry Sensor Interface 10–20. The design of each wireless system is discussed in relation to its impact on the system’s usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.

Direct classification of all American English phonemes using signals from functional speech motor cortex

Abstract: Objective. Although brain–computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we sought to decode elements of speech production using ECoG. Approach. We investigated words that contain the entire set of phonemes in the general American accent using ECoG with four subjects. Using a linear classifier, we evaluated the degree to which individual phonemes within each word could be correctly identified from cortical signal. Main results. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. Significance. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits s −1 (33.6 words min −1 ), supporting pursuit of speech articulation for BCI control.

Investigation of tDCS volume conduction effects in a highly realistic head model

Abstract: Objective. We investigate volume conduction effects in transcranial direct current stimulation (tDCS) and present a guideline for efficient and yet accurate volume conductor modeling in tDCS using our newly-developed finite element (FE) approach. Approach. We developed a new, accurate and fast isoparametric FE approach for high-resolution geometry-adapted hexahedral meshes and tissue anisotropy. To attain a deeper insight into tDCS, we performed computer simulations, starting with a homogenized three-compartment head model and extending this step by step to a six-compartment anisotropic model. Main results. We are able to demonstrate important tDCS effects. First, we find channeling effects of the skin, the skull spongiosa and the cerebrospinal fluid compartments. Second, current vectors tend to be oriented towards the closest higher conducting region. Third, anisotropic WM conductivity causes current flow in directions more parallel to the WM fiber tracts. Fourth, the highest cortical current magnitudes are not only found close to the stimulation sites. Fifth, the median brain current density decreases with increasing distance from the electrodes. Significance. Our results allow us to formulate a guideline for volume conductor modeling in tDCS. We recommend to accurately model the major tissues between the stimulating electrodes and the target areas, while for efficient yet accurate modeling, an exact representation of other tissues is less important. Because for the low-frequency regime in electrophysiology the quasi-static approach is justified, our results should also be valid for at least low-frequency (e.g., below 100 Hz) transcranial alternating current stimulation.

Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

Abstract: Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition‐based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.

Blood pressure control with selective vagal nerve stimulation and minimal side effects

Abstract: Objective. Hypertension is the largest threat to patient health and a burden to health care systems. Despite various options, 30% of patients do not respond sufficiently to medical treatment. Mechanoreceptors in the aortic arch relay blood pressure (BP) levels through vagal nerve (VN) fibers to the brainstem and trigger the baroreflex, lowering the BP. Selective electrical stimulation of these nerve fibers reduced BP in rats. However, there is no technique described to localize and stimulate these fibers inside the VN without inadvertent stimulation of non-baroreceptive fibers causing side effects like bradycardia and bradypnea. Approach. We present a novel method for selective VN stimulation to reduce BP without the aforementioned side effects. Baroreceptor compound activity of rat VN (n = 5) was localized using a multichannel cuff electrode, true tripolar recording and a coherent averaging algorithm triggered by BP or electrocardiogram. Main results. Tripolar stimulation over electrodes near the barofibers reduced the BP without triggering significant bradycardia and bradypnea. The BP drop was adjusted to 60% of the initial value by varying the stimulation pulse width and duration, and lasted up to five times longer than the stimulation. Significance. The presented method is robust to impedance changes, independent of the electrode's relative position, does not compromise the nerve and can run on implantable, ultra-low power signal processors.

Electrophysiology-based detection of emergency braking intention in real-world driving.

Abstract: Objective. The fact that all human action is preceded by brain processes partially observable through neuroimaging devices such as electroencephalography (EEG) is currently being explored in a number of applications. A recent study by Haufe et al (2011 J. Neural Eng. 8 056001) demonstrates the possibility of performing fast detection of forced emergency brakings during driving based on EEG and electromyography, and discusses the use of such neurotechnology for braking assistance systems. Since the study was conducted in a driving simulator, its significance regarding real-world applicability needs to be assessed. Approach. Here, we replicate that experimental paradigm in a real car on a non-public test track. Main results. Our results resemble those of the simulator study, both qualitatively (in terms of the neurophysiological phenomena observed and utilized) and quantitatively (in terms of the predictive improvement achievable using electrophysiology in addition to behavioral measures). Moreover, our findings are robust with respect to a temporary secondary auditory task mimicking verbal input from a fellow passenger. Significance. Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).

Selectivity of direct and network-mediated stimulation of the retinal ganglion cells with epi-, sub- and intraretinal electrodes.

Abstract: Objective. Intra-retinal placement of stimulating electrodes can provide close and stable proximity to target neurons. We assessed improvement in stimulation thresholds and selectivity of the direct and network-mediated retinal stimulation with intraretinal electrodes, compared to epiretinal and subretinal placements. Approach. Stimulation thresholds of the retinal ganglion cells (RGCs) in wild-type rat retina were measured using the patch-clamp technique. Direct and network-mediated responses were discriminated using various synaptic blockers. Main results. Three types of RGC responses were identified: short latency (SL, τ 40 ms) originating in photoreceptors. Cathodic epiretinal stimulation exhibited the lowest threshold for direct RGC response and the highest direct selectivity (network/direct thresholds ratio), exceeding a factor of 3 with pulse durations below 0.5 ms. For network-mediated stimulation, the lowest threshold was obtained with anodic pulses in OPL position, and its network selectivity (direct/network thresholds ratio) increased with pulse duration, exceeding a factor of 4 at 10 ms. Latency of all three types of responses decreased with increasing strength of the stimulus. Significance. These results define the optimal range of pulse durations, pulse polarities and electrode placement for the retinal prostheses aiming at direct or network-mediated stimulation of RGCs.

P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.

Abstract: Objective. In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et?al (2012 Psychophysiology 49 1449?53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system. Approach. We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain?computer interface (BCI) application. N = 13 individuals repeatedly performed a 6???6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical. Main results. Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77). Significance. We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG.

Abstract: Objective. Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach. We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main results. Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts' Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts' Law tasks with high levels of path efficiency. Significance. These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control.

Long-term reliability of Al2O3?and Parylene C bilayer encapsulated Utah electrode array based neural interfaces for chronic implantation

Abstract: Objective. We focus on improving the long-term stability and functionality of neural interfaces for chronic implantation by using bilayer encapsulation. Approach. We evaluated the long-term reliability of Utah electrode array (UEA) based neural interfaces encapsulated by 52?nm of atomic layer deposited Al2O3?and 6??m of Parylene C bilayer, and compared these to devices with the baseline Parylene-only encapsulation. Three variants of arrays including wired, wireless, and active UEAs were used to evaluate this bilayer encapsulation scheme, and were immersed in phosphate buffered saline (PBS) at 57??C for accelerated lifetime testing. Main results. The median tip impedance of the bilayer encapsulated wired UEAs increased from 60 to 160 k? during the 960?days of equivalent soak testing at 37??C, the opposite trend to that typically observed for Parylene encapsulated devices. The loss of the iridium oxide tip metallization and etching of the silicon tip in PBS solution contributed to the increase of impedance. The lifetime of fully integrated wireless UEAs was also tested using accelerated lifetime measurement techniques. The bilayer coated devices had stable power-up frequencies at ?910?MHz and constant radio-frequency signal strength of??50 dBm during up to 1044?days (still under testing) of equivalent soaking time at 37??C. This is a significant improvement over the lifetime of ?100?days achieved with Parylene-only encapsulation at 37??C. The preliminary samples of bilayer coated active UEAs with a flip-chip bonded ASIC chip had a steady current draw of ?3?mA during 228?days of soak testing at 37??C. An increase in the current draw has been consistently correlated to device failures, so is a sensitive metric for their lifetime. Significance. The trends of increasing electrode impedance of wired devices and performance stability of wireless and active devices support the significantly greater encapsulation performance of this bilayer encapsulation compared with Parylene-only encapsulation. The bilayer encapsulation should significantly improve the in vivo lifetime of neural interfaces for chronic implantation.

Stereovision and augmented reality for closed-loop control of grasping in hand prostheses

Abstract: Objective. Technologically advanced assistive devices are nowadays available to restore grasping, but effective and effortless control integrating both feed-forward (commands) and feedback (sensory information) is still missing. The goal of this work was to develop a user friendly interface for the semi-automatic and closed-loop control of grasping and to test its feasibility. Approach. We developed a controller based on stereovision to automatically select grasp type and size and augmented reality (AR) to provide artificial proprioceptive feedback. The system was experimentally tested in healthy subjects using a dexterous hand prosthesis to grasp a set of daily objects. The subjects wore AR glasses with an integrated stereo-camera pair, and triggered the system via a simple myoelectric interface. Main results. The results demonstrated that the subjects got easily acquainted with the semi-autonomous control. The stereovision grasp decoder successfully estimated the grasp type and size in realistic, cluttered environments. When allowed (forced) to correct the automatic system decisions, the subjects successfully utilized the AR feedback and achieved close to ideal system performance. Significance. The new method implements a high level, low effort control of complex functions in addition to the low level closed-loop control. The latter is achieved by providing rich visual feedback, which is integrated into the real life environment. The proposed system is an effective interface applicable with small alterations for many advanced prosthetic and orthotic/therapeutic rehabilitation devices.

To sort or not to sort: the impact of spike-sorting on neural decoding performance

Abstract: Objective. Brain–computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. Approach. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Main results. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Significance. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

Abstract: Objective Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning Only recently zero-training methods have become a subject of study This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs) For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping Approach A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects The individual influence of the involved components (a)–(d) are investigated Main results Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation Significance A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling Recording calibration data for a supervised BCI would require valuable time which is lost for spelling The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach It could be of use for various clinical and non-clinical ERP-applications of BCI

Differential responses to high-frequency electrical stimulation in ON and OFF retinal ganglion cells

Abstract: Objective. The field of retinal prosthetics for artificial vision has advanced considerably in recent years, however clinical outcomes remain inconsistent. The performance of retinal prostheses is likely limited by the inability of electrical stimuli to preferentially activate different types of retinal ganglion cell (RGC). Approach. Here we examine the response of rabbit RGCs to high-frequency stimulation, using biphasic pulses applied at 2000 pulses per second. Responses were recorded using cell-attached patch clamp methods, and stimulation was applied epiretinally via a small cone electrode. Main results. When prolonged stimulus trains were applied to OFF-brisk transient (BT) RGCs, the cells exhibited a non-monotonic relationship between response strength and stimulus amplitude; this response pattern was different from those elicited previously by other electrical stimuli. When the amplitude of the stimulus was modulated transiently from a non-zero baseline amplitude, ON-BT and OFF-BT cells exhibited different activity patterns: ON cells showed an increase in activity while OFF cells exhibited a decrease in activity. Using a different envelope to modulate the amplitude of the stimulus, we observed the opposite effect: ON cells exhibited a decrease in activity while OFF cells show an increase in activity. Significance. As ON and OFF RGCs often exhibit opposing activity patterns in response to light stimulation, this work suggests that high-frequency electrical stimulation of RGCs may be able to elicit responses that are more physiological than traditional pulsatile stimuli. Additionally, the prospect of an electrical stimulus capable of cell-type specific selective activation has broad applications throughout the fields of neural stimulation and neuroprostheses.

An independent SSVEP-based brain-computer interface in locked-in syndrome

Abstract: Objective. Steady-state visually evoked potential (SSVEP)-based brain–computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Approach. Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based ‘interlaced squares’ stimulation pattern. Main results. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. Significance. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback

Abstract: Objective. Present day cortical brain–machine interfaces (BMIs) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closedloop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available. Approach. To overcome this limitation, this study tested a closed-loop BMI that utilizes intracortical microstimulation to provide ‘tactile’ sensation to a non-human primate. Main result. Using stimulation electrodes in Brodmann area 1 of somatosensory cortex (BA1) and recording electrodes in the anterior intraparietal area, the parietal reach region and dorsal area 5 (area 5d), it was found that this form of feedback can be used in BMI tasks. Significance. Providing somatosensory feedback has the poyential to greatly improve the performance of cognitive neuroprostheses especially for fine control and object manipulation. Adding stimulation to a BMI system could therefore improve the quality of life for severely paralyzed patients.

Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex.

Abstract: Objective. Action potentials and local field potentials (LFPs) recorded in primary motor cortex contain information about the direction of movement. LFPs are assumed to be more robust to signal instabilities than action potentials, which makes LFPs, along with action potentials, a promising signal source for brain–computer interface applications. Still, relatively little research has directly compared the utility of LFPs to action potentials in decoding movement direction in human motor cortex. Approach. We conducted intracortical multi-electrode recordings in motor cortex of two persons (T2 and [S3]) as they performed a motor imagery task. We then compared the offline decoding performance of LFPs and spiking extracted from the same data recorded across a one-year period in each participant. Main results. We obtained offline prediction accuracy of movement direction and endpoint velocity in multiple LFP bands, with the best performance in the highest (200–400 Hz) LFP frequency band, presumably also containing low-pass filtered action potentials. Cross-frequency correlations of preferred directions and directional modulation index showed high similarity of directional information between action potential firing rates (spiking) and high frequency LFPs (70–400 Hz), and increasing disparity with lower frequency bands (0–7, 10–40 and 50–65 Hz). Spikes predicted the direction of intended movement more accurately than any individual LFP band, however combined decoding of all LFPs was statistically indistinguishable from spike-based performance. As the quality of spiking signals (i.e. signal amplitude) and the number of significantly modulated spiking units decreased, the offline decoding performance decreased 3.6[5.65]%/month (for T2 and [S3] respectively). The decrease in the number of significantly modulated LFP signals and their decoding accuracy followed a similar trend (2.4[2.85]%/month, ANCOVA, p = 0.27[0.03]). Significance. Field potentials provided comparable offline decoding performance to unsorted spikes. Thus, LFPs may provide useful external device control using current human intracortical recording technology. (Clinical trial registration number: NCT00912041.)

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

Abstract: Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface.

Abstract: Objective. The bedside detection of potential awareness in patients with disorders of consciousness (DOC) currently relies only on behavioral observations and tests; however, the misdiagnosis rates in this patient group are historically relatively high. In this study, we proposed a visual hybrid brain–computer interface (BCI) combining P300 and steady-state evoked potential (SSVEP) responses to detect awareness in severely brain injured patients. Approach. Four healthy subjects, seven DOC patients who were in a vegetative state (VS, n = 4) or minimally conscious state (MCS, n = 3), and one locked-in syndrome (LIS) patient attempted a command-following experiment. In each experimental trial, two photos were presented to each patient; one was the patientʼs own photo, and the other photo was unfamiliar. The patients were instructed to focus on their own or the unfamiliar photos. The BCI system determined which photo the patient focused on with both P300 and SSVEP detections. Main results. Four healthy subjects, one of the 4 VS, one of the 3 MCS, and the LIS patient were able to selectively attend to their own or the unfamiliar photos (classification accuracy, 66–100%). Two additional patients (one VS and one MCS) failed to attend the unfamiliar photo (50–52%) but achieved significant accuracies for their own photo (64–68%). All other patients failed to show any significant response to commands (46–55%). Significance. Through the hybrid BCI system, command following was detected in four healthy subjects, two of 7 DOC patients, and one LIS patient. We suggest that the hybrid BCI system could be used as a supportive bedside tool to detect awareness in patients with DOC.

An optimized ERP Brain-computer interface based on facial expression changes

Abstract: Objective. Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain–computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. Approach. Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. Main results. The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05). Significance. The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.

Performance measurement for brain-computer or brain-machine interfaces: a tutorial

Abstract: Objective. Brain–computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach. A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main results. Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. Significance. Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.

Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials

Abstract: Objective. Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity 'number of misclassifications' and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied. Approach. Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a naive Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed. Main results. On average, 74.5 ± 13.8% and 82.2 ± 10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083). Significance. A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain–computer interface applications with this group of patients.

Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates.

Abstract: Objective. Intracortical brain–machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control. Approach. We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0–150 Hz range. Main results. While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0–80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80–150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0–40, 40–80, and 80–150 Hz) of the 0–150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control. Significance. Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control.

CNT/PDMS-based canal-typed ear electrodes for inconspicuous EEG recording

Abstract: Objective. Current electroencephalogram (EEG) monitoring systems typically require cumbersome electrodes that must be pasted on a scalp, making a private recording of an EEG in a public place difficult. We have developed a small, user friendly, biocompatible electrode with a good appearance for inconspicuous EEG monitoring. Approach. We fabricated carbon nanotube polydimethylsiloxane (CNT/PDMS)-based canal-type ear electrodes (CEE) for EEG recording. These electrodes have an additional function, triggering sound stimulation like earphones and recording EEG simultaneously for auditory brain–computer interface (BCI). The electrode performance was evaluated by a standard EEG measurement paradigm, including the detection of alpha rhythms and measurements of N100 auditory evoked potential (AEP), steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR). Furthermore, the bio- and skin-compatibility of CNT/PDMS were tested. Main results. All feasibility studies were successfully recorded with the fabricated electrodes, and the biocompatibility of CNT/PDMS was also proved. Significance. These electrodes could be used to monitor EEG clinically, in ubiquitous health care and in brain–computer interfaces.

Controllable pulse parameter transcranial magnetic stimulator with enhanced circuit topology and pulse shaping

Abstract: Objective. This work aims at flexible and practical pulse parameter control in transcranial magnetic stimulation (TMS), which is currently very limited in commercial devices. Approach. We present a third generation controllable pulse parameter device (cTMS3) that uses a novel circuit topology with two energy-storage capacitors. It incorporates several implementation and functionality advantages over conventional TMS devices and other devices with advanced pulse shape control. cTMS3 generates lower internal voltage differences and is implemented with transistors with a lower voltage rating than prior cTMS devices. Main results. cTMS3 provides more flexible pulse shaping since the circuit topology allows four coil-voltage levels during a pulse, including approximately zero voltage. The near-zero coil voltage enables snubbing of the ringing at the end of the pulse without the need for a separate active snubber circuit. cTMS3 can generate powerful rapid pulse sequences ( 10 ms inter pulse interval) by increasing the width of each subsequent pulse and utilizing the large capacitor energy storage, allowing the implementation of paradigms such as paired-pulse and quadripulse TMS with a single pulse generation circuit. cTMS3 can also generate theta (50 Hz) burst stimulation with predominantly unidirectional electric field pulses. The cTMS3 device functionality and output strength are illustrated with electrical output measurements as well as a study of the effect of pulse width and polarity on the active motor threshold in ten healthy volunteers. Significance. The cTMS3 features could extend the utility of TMS as a research, diagnostic, and therapeutic tool.

Intention estimation in brain-machine interfaces.

Abstract: Objective. The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported ‘recalibrated feedback intention-trained Kalman Filter’ (ReFIT-KF). Approach. This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm. Main results. Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied. Significance. These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.